ListarGI-GAC - Artigos por tema "Federated learning"

Mostrando ítems 1-3 de 3

    • Concept Drift Detection and Adaptation for Federated and Continual Learning 

      Casado, Fernando E.; Lema, Dylan; Criado, Marcos F.; Iglesias, Roberto; Regueiro, Carlos V.; Barro, Senén (Springer, 2021)
      [Abstract] Smart devices, such as smartphones, wearables, robots, and others, can collect vast amounts of data from their environment. This data is suitable for training machine learning models, which can significantly ...

    • Ensemble and continual federated learning for classification tasks 

      Casado, Fernando E.; Lema, Dylan; Iglesias, Roberto; Regueiro, Carlos V.; Barro, Senén (Springer, 2023-09)
      [Abstract]: Federated learning is the state-of-the-art paradigm for training a learning model collaboratively across multiple distributed devices while ensuring data privacy. Under this framework, different algorithms have ...

    • Non-IID data and Continual Learning processes in Federated Learning: A long road ahead 

      Criado, Marcos F.; Casado, Fernando E.; Iglesias Rodríguez, Roberto; Regueiro, Carlos V.; Barro, Senén (Elsevier, 2022)
      [Abstract] Federated Learning is a novel framework that allows multiple devices or institutions to train a machine learning model collaboratively while preserving their data private. This decentralized approach is prone ...